Structure modeling method and device, storage medium and electronic equipment

Abstract

The application discloses a structure modeling method and device, a storage medium and an electronic device. The method comprises the following steps: determining a plurality of initial parameters and historical data corresponding to the plurality of initial parameters based on a historical structure data set, wherein the historical structure data set is obtained based on data of a structure of a preset type in a historical construction project; performing classification processing on the plurality of initial parameters to obtain target correlation information between the plurality of initial parameters; determining priority coefficients corresponding to the plurality of initial parameters respectively according to the target correlation information; determining target parameter values corresponding to the plurality of initial parameters respectively based on the historical data and the priority coefficients; and performing three-dimensional modeling on the target parameter values corresponding to the plurality of initial parameters respectively to obtain a target three-dimensional model. The application solves the technical problem that, due to the fact that a structure is greatly influenced by a specific environment, a generated three-dimensional model is greatly different, modeling efficiency is low, and human resources are consumed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic information, in particular to a structure modeling method and device, a storage medium and an electronic device. BACKGROUND

[0002] At present, engineering design schemes of different structures and different working conditions are quite different. If different design schemes need to create three-dimensional models separately, it will lead to a lot of work and low efficiency. In addition, structure engineering design involves the cooperation of different design units, and different three-dimensional design platforms are used by different design units, so the model quantity and format are different, which causes great difficulty in the interaction of three-dimensional model data. Moreover, when different design units design the same structure, they are easy to build models with different granularities based on different modeling design principles and parameter settings, which is not conducive to three-dimensional collaborative design among different design units, reduces the modeling efficiency, and causes waste of human and time costs.

[0003] At present, there is no effective solution to the above problems. SUMMARY

[0004] The embodiments of the present application provide a structure modeling method, device, storage medium and electronic device, to at least solve the technical problem that the generated three-dimensional model is greatly different due to the great influence of the structure on the specific environment, which causes low modeling efficiency and high human resource consumption.

[0005] According to an aspect of the embodiments of the present application, a structure modeling method is provided, comprising: determining a plurality of initial parameters and historical data corresponding to the plurality of initial parameters based on a historical structure data set, wherein the historical structure data set is obtained based on data of a historical construction project of a preset type of structure; performing classification processing on the plurality of initial parameters to obtain target association information between the plurality of initial parameters; determining priority coefficients corresponding to the plurality of initial parameters respectively according to the target association information; determining target parameter values corresponding to the plurality of initial parameters respectively based on the historical data and the priority coefficients; and performing three-dimensional modeling on the target parameter values corresponding to the plurality of initial parameters respectively to obtain a target three-dimensional model.

[0006] Optionally, the classifying processing on the plurality of initial parameters to obtain the target association information between the plurality of initial parameters comprises: performing first-level classifying processing on the plurality of initial parameters based on construction types to obtain first-level classification results corresponding to the plurality of initial parameters respectively; performing second-level classifying processing on the plurality of initial parameters based on construction methods to obtain second-level classification results corresponding to the plurality of initial parameters respectively; performing third-level classifying processing on the plurality of initial parameters based on historical naming manners to obtain third-level classification results corresponding to the plurality of initial parameters respectively; determining feature information corresponding to the plurality of initial parameters respectively according to the first-level classification results, the second-level classification results, and the third-level classification results, wherein the feature information at least comprises a weight value; and obtaining the target association information between the plurality of initial parameters based on the weight value.

[0007] Optionally, the obtaining the target association information between the plurality of initial parameters based on the weight value comprises: determining an initial parameter with a weight value greater than a preset weight threshold value in the plurality of initial parameters as a parent node, wherein the parent node is a superior parameter in the target association information; determining an initial parameter with a weight value less than or equal to the weight threshold value in the plurality of initial parameters as a child node, wherein the child node is a subordinate parameter in the target association information; and determining the target association information between the plurality of initial parameters based on the parent node and the child node.

[0008] Optionally, in a case where the feature information comprises variability information, the variability information comprises a variable quantity and a dependent quantity, the variable quantity is a variable in the plurality of initial parameters and is an independent variable, and the dependent quantity is a variable in the plurality of initial parameters and is a dependent variable, the determining the target association information between the plurality of initial parameters based on the parent node and the child node comprises: performing screening processing on the parent node and the child node based on a preset constraint condition to obtain the parent node and the child node satisfying the constraint condition, wherein the constraint condition is that an initial parameter corresponding to the variable quantity does not exist in the parent node and a same-level node, and an initial parameter corresponding to the dependent quantity exists in at least one of the parent node or the same-level node, the same-level node being an initial parameter corresponding to the same weight value in the plurality of initial parameters; and determining the target association information between the plurality of initial parameters based on the parent node and the child node satisfying the constraint condition.

[0009] Optionally, the determining, based on the historical data and the priority coefficients, target parameter values corresponding to the plurality of initial parameters respectively, comprises: determining first priority coefficients greater than a preset first threshold in the priority coefficients corresponding to the plurality of initial parameters respectively; taking the initial parameter corresponding to the first priority coefficient as an adjustment input bit, wherein the adjustment input bit is used to obtain a preset initial value; determining second priority coefficients less than or equal to the first threshold in the plurality of priority coefficients corresponding to the plurality of initial parameters respectively; taking the initial parameter corresponding to the second priority coefficient as a calculation fill-in bit, wherein the calculation fill-in bit is used to obtain data generated based on a preset calculation rule; determining a fill-in value corresponding to the calculation fill-in bit based on the historical data, the calculation rule, and the target association information; and obtaining the target parameter values corresponding to the plurality of initial parameters respectively according to the initial value and the fill-in value.

[0010] Optionally, the three-dimensional modeling according to the target parameter values corresponding to the plurality of initial parameters respectively to obtain a target three-dimensional model comprises: determining whether the priority coefficients corresponding to the plurality of initial parameters are greater than a preset second threshold; if the priority coefficients corresponding to the plurality of initial parameters are greater than the second threshold, determining a to-be-checked parameter in the plurality of initial parameters whose priority coefficient is greater than the second threshold; taking the target parameter value corresponding to the to-be-checked parameter as a to-be-checked value; determining whether the to-be-checked value satisfies a preset checking rule; and if the to-be-checked value satisfies the preset checking rule, three-dimensional modeling according to the target parameter values corresponding to the plurality of initial parameters respectively to obtain the target three-dimensional model.

[0011] Optionally, the determining, based on the historical building data set, a plurality of initial parameters and historical data corresponding to the plurality of initial parameters, comprises: obtaining a plurality of data formats corresponding to the historical building data set; determining an intermediate format corresponding to the plurality of data formats, wherein the intermediate format is a data format having interaction capability with the plurality of data formats; and determining the plurality of initial parameters and the historical data corresponding to the plurality of initial parameters based on the historical building data set and the intermediate format.

[0012] According to another aspect of the embodiment of the present application, there is further provided a structure modeling device, comprising: a first determining module configured to determine a plurality of initial parameters and historical data corresponding to the plurality of initial parameters based on a historical structure dataset, wherein the historical structure dataset is obtained based on data of structures of a preset category in historical construction projects; a classifying module configured to perform classification processing on the plurality of initial parameters to obtain target correlation information between the plurality of initial parameters; a second determining module configured to determine priority coefficients corresponding to the plurality of initial parameters respectively according to the target correlation information; a third determining module configured to determine target parameter values corresponding to the plurality of initial parameters respectively based on the historical data and the priority coefficients; and an obtaining module configured to perform three-dimensional modeling on the target parameter values corresponding to the plurality of initial parameters respectively to obtain a target three-dimensional model.

[0013] In the embodiment of the present application, the plurality of initial parameters and the historical data corresponding to the plurality of initial parameters are determined based on a historical structure dataset, wherein the historical structure dataset is obtained based on data of structures of a preset category in historical construction projects; the classification processing is performed on the plurality of initial parameters to obtain target correlation information between the plurality of initial parameters; the priority coefficients corresponding to the plurality of initial parameters respectively are determined according to the target correlation information; the target parameter values corresponding to the plurality of initial parameters respectively are determined based on the historical data and the priority coefficients; and the three-dimensional modeling is performed on the target parameter values corresponding to the plurality of initial parameters respectively to obtain a target three-dimensional model. The purpose of automatically generating a structure model based on historical data is achieved, thereby realizing the technical effects of improving modeling efficiency, improving design interactivity, and further reducing consumption of human resources, thereby solving the technical problem of large difference between generated three-dimensional models due to great influence of structures on specific environments, low modeling efficiency, and high consumption of human resources. BRIEF DESCRIPTION OF DRAWINGS

[0014] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and serve to explain the principles of the application, and do not limit the application in any way. In the drawings:

[0015] Figure 1 is a schematic diagram of an optional structure modeling method according to an embodiment of the present application;

[0016] Figure 2 is a flowchart of an optional structure modeling method according to an embodiment of the present application;

[0017] Figure 3 is a data schematic diagram of an optional structure modeling method according to an embodiment of the present application;

[0018] Figure 4 is a flow chart of another optional construction modeling method according to an embodiment of the present application;

[0019] Figure 5 is an interactive flow chart of an optional construction modeling method according to an embodiment of the present application;

[0020] Figure 6 is a schematic diagram of an optional construction modeling device according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0022] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0023] In order to facilitate understanding, the following specific terms are explained:

[0024] Python, a programming language, combines interactivity, compilation, interpretation, and object-oriented program scripting language.

[0025] According to an embodiment of the present application, a construction modeling method embodiment is provided. It should be noted that the steps shown in the flow chart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flow chart, in some cases, the steps shown or described herein can be executed in an order different from that herein.

[0026] Figure 1 is a schematic diagram of an optional construction modeling method according to an embodiment of the present application, as Figure 1As shown, the method comprises the following steps:

[0027] In step S102, a plurality of initial parameters and historical data corresponding to the plurality of initial parameters are determined based on a historical construction dataset, wherein the historical construction dataset is obtained based on data of construction projects of a preset type in history.

[0028] It can be understood that, in order to utilize the existing data in the historical construction project to generate a construction model and improve the modeling efficiency, a plurality of initial parameters corresponding to the requirements of the current project need to be determined, and the historical data corresponding to the range of the plurality of initial parameters needs to be determined.

[0029] Optionally, the historical construction dataset can be of multiple types, for example, obtained based on drawings extracted from historical construction projects or based on parameters set in historical construction projects, etc.

[0030] Optionally, the preset type of construction can be of multiple types, for example, the construction can be a vertical shaft of a cable tunnel.

[0031] It should be noted that, due to the influence of the surrounding environment and various underground pipelines on actual cable projects, the design schemes of different cable tunnel scales and different working conditions are quite different. If a three-dimensional model needs to be created for each design scheme, it will be laborious and inefficient, which does not conform to the parameterized design principle of three-dimensional design of cable projects. By utilizing the design schemes and parameter settings in the historical construction project, the modeling efficiency can be greatly improved.

[0032] In an optional embodiment, the determination of the plurality of initial parameters and the historical data corresponding to the plurality of initial parameters based on the historical construction dataset comprises: obtaining a plurality of data formats corresponding to the historical construction dataset; determining an intermediate format corresponding to the plurality of data formats, wherein the intermediate format is a data format that has interaction capability with the plurality of data formats; and determining the plurality of initial parameters and the historical data corresponding to the plurality of initial parameters based on the historical construction dataset and the intermediate format.

[0033] It can be understood that, due to the existence of multiple design units collaborating in actual construction projects, in order to improve the collaboration efficiency, an intermediate format that meets the interaction requirements needs to be determined. Through the above processing, the intermediate format breaks down the barriers between three-dimensional modeling platforms, which is conducive to improving the collaboration efficiency.

[0034] In step S104, the plurality of initial parameters are classified to obtain target association information between the plurality of initial parameters.

[0035] It can be understood that there is a correlation between the plurality of initial parameters in the structure modeling project, and in order to obtain the target correlation information, the plurality of initial parameters need to be classified. Through the above processing, the superior-inferior relationship between the plurality of initial parameters can be obtained. Facilitating the subsequent steps of linkage calculation and processing, and improving the modeling calculation efficiency.

[0036] Optionally, the target correlation information can be of various types, for example: the target correlation information is a tree diagram.

[0037] It should be noted that in the design of the structure, there is a superior-inferior relationship between the initial parameters. In order to facilitate understanding, specific examples are given, for example: the initial parameters include parameters corresponding to a plurality of components such as shaft body, primary lining, protective layer, secondary lining, etc. According to the design logic, the shaft body includes the primary lining, the protective layer, the secondary lining and other sub-components, so the primary lining, the protective layer and the secondary lining are taken as the subordinate parameters, and the shaft body is taken as the superior parameter.

[0038] In an optional embodiment, the classification processing of the plurality of initial parameters to obtain the target correlation information between the plurality of initial parameters includes: performing one-level classification processing on the plurality of initial parameters based on the structure type to obtain one-level classification results corresponding to the plurality of initial parameters respectively; performing two-level classification processing on the plurality of initial parameters based on the structure method to obtain two-level classification results corresponding to the plurality of initial parameters respectively; performing three-level classification processing on the plurality of initial parameters based on the historical naming method to obtain three-level classification results corresponding to the plurality of initial parameters respectively; determining feature information corresponding to the plurality of initial parameters according to the one-level classification results, the two-level classification results, and the three-level classification results, wherein the feature information at least includes a weight value; and obtaining the target correlation information between the plurality of initial parameters based on the weight value.

[0039] It can be understood that the plurality of initial parameters are classified and processed, and the classified initial parameters are processed to obtain feature information. In order to obtain the target correlation information, the feature information at least includes a weight value, the weight value is allocated based on the weight value, and then the target correlation information between the plurality of initial parameters is distinguished. Through the above processing, the initial parameters are preprocessed, and through the classification processing and the processing of assigning parameters to features, the initial parameters are called in the model generation calculation process, and the efficiency of generating the target model is improved.

[0040] Optionally, the structure type can be of various types, for example: the same structure can be divided into general type and special type, in other words, the general type is designed according to the industry standard, and the special type is designed as a special type.

[0041] Optionally, the construction method can be various, for example, different construction methods are used for the same structure, which will cause structural differences. The shaft of the cable tunnel can be divided into open shaft, excavated shaft, shielded shaft and pipe jacking shaft.

[0042] Optionally, the historical naming method can be various, for example, the historical naming method can reflect the main information of the structure. The industry has a relatively fixed way of calling a certain structure. Based on the historical naming method, the accuracy of the target association information between the initial parameters can be improved. For example, for the shaft of the cable tunnel, the historical naming method includes 3M(meter)*4M straight well and φ(diameter)4M three-way well. When there is a similar demand, the corresponding historical data in the historical construction project can be conveniently obtained.

[0043] Optionally, the initial parameters are standardized. Because different historical construction projects can be designed by different designers according to their design habits, the naming method of the initial parameters will change with the design habits, which will affect the generated model. Therefore, the historical data in the historical construction project, such as the shape of the shaft on the shaft drawing, is layered and uniformly named for each part, such as well cavity, soil layer, branch segment, connection segment, well wall, cushion layer and protection layer.

[0044] Optionally, the feature information can be various, for example, the first classification result, the second classification result and the third classification result can be assigned with features. The feature information can be set as explicit or implicit, attribute type (decimal, integer, text, Boolean, drop-down, etc.), readability, calculation rule (mathematical relationship such as addition, subtraction, multiplication, division and trigonometric function), initial value and feature point of the initial parameter.

[0045] In an optional embodiment, based on the weight value, the target association information between the plurality of initial parameters is obtained, including: determining the initial parameter with the weight value greater than the preset weight threshold value in the plurality of initial parameters as a parent node, wherein the parent node is a superior parameter in the target association information; determining the initial parameter with the weight value less than or equal to the weight threshold value in the plurality of initial parameters as a child node, wherein the child node is a subordinate parameter in the target association information; determining the target association information between the plurality of initial parameters based on the parent node and the child node.

[0046] It can be understood that the parent node and the child node in the initial parameter are determined by the size relationship between the weight value corresponding to the initial parameter and the weight threshold value. Through the above processing, the initial parameters are further classified, and the target association information is obtained based on the parent node and the child node.

[0047] It should be noted that the target association information between the initial variables is described by a tree diagram, the position of the initial parameters on the tree diagram is determined according to the size of the weight value, the weight value greater than the weight threshold is taken as the parent node, the secondary variables dominated by the parent node and having a weight lower than the weight threshold are taken as the child nodes, and the initial variables having the same weight value and affecting each other are taken as the sibling nodes at the same level, thereby obtaining the weight relationship and grouping state between the initial variables.

[0048] In an optional embodiment, when the feature information includes variability information, the variability information includes variable variables and random variables, the variable variables are variables in the plurality of initial parameters and are not dependent variables, the random variables are variables in the plurality of initial parameters and are dependent variables, and the target association information between the plurality of initial parameters is determined based on the parent nodes and the child nodes, the method comprises: performing screening processing on the parent nodes and the child nodes based on a preset constraint condition to obtain the parent nodes and the child nodes satisfying the constraint condition, wherein the constraint condition is that the initial parameters corresponding to the variable variables do not have the parent nodes and the sibling nodes, and the initial parameters corresponding to the random variables have at least one of the parent nodes or the sibling nodes, and the sibling nodes are the initial parameters corresponding to the initial parameters having the same weight value in the plurality of initial parameters; and determining the target association information between the plurality of initial parameters based on the parent nodes and the child nodes satisfying the constraint condition.

[0049] It can be understood that the initial variables correspond to feature information, and the variability information is included in the feature information. The initial parameters having the variability information as variable variables do not have parent nodes and sibling nodes, and the variability information as random variables must have parent nodes or sibling nodes. Based on the variability information, the parent nodes and the sibling nodes satisfying the constraint condition are obtained by performing screening processing based on a preset constraint condition. The target association information between the plurality of initial parameters is determined based on the parent nodes and the child nodes satisfying the constraint condition. Through the above processing, the parent nodes and the child nodes are further processed, which is beneficial to improve the accuracy of the target association information, and further improve the modeling efficiency and accuracy.

[0050] Optionally, the variability information further includes a fixed amount, and the fixed amount exists as a fixed value and is independent of the target association information, for example, the fixed amount is a fixed coefficient and a constant required for generating a model.

[0051] In step S106, the priority coefficients corresponding to the plurality of initial parameters are determined based on the target association information.

[0052] It can be understood that the priority coefficients are obtained based on the target association information and are used to represent the domination relationship, i.e., the mutual influence and change relationship, between the plurality of initial parameters.

[0053] In step S108, the target parameter values corresponding to the plurality of initial parameters are determined based on the historical data and the priority coefficients.

[0054] It can be understood that the historical data is used as the source, and the priority coefficients are used to determine the dominance relationship between the plurality of initial parameters, which reflects the linkage transformation between the initial parameters. Through the above processing, the target parameter values corresponding to the plurality of initial parameters are determined based on the target association information and the priority. By automatically determining the values of the initial parameters, i.e., the target parameter values, the complex and lengthy parameter setting and processing by manual operation are avoided. The setting efficiency of the target parameter values is effectively improved, thereby improving the modeling efficiency, and reducing the possibility of errors caused by manual setting and calculation.

[0055] In an optional embodiment, the determination of the target parameter values corresponding to the plurality of initial parameters based on the historical data and the priority coefficients includes: determining a first priority coefficient greater than a preset first threshold value in the priority coefficients corresponding to the plurality of initial parameters; taking the initial parameter corresponding to the first priority coefficient as an adjustment input bit, wherein the adjustment input bit is used to obtain a preset initial value; determining a second priority coefficient less than or equal to the first threshold value in the plurality of priority coefficients corresponding to the plurality of initial parameters; taking the initial parameter corresponding to the second priority coefficient as a calculation fill-in bit, wherein the calculation fill-in bit is used to obtain data generated based on a preset calculation rule; determining a fill-in value corresponding to the calculation fill-in bit based on the historical data, the calculation rule, and the target association information; and obtaining the target parameter values corresponding to the plurality of initial parameters according to the initial value and the fill-in value.

[0056] It can be understood that in order to meet the actual project requirements, part of the data needs to be set according to the specific situation, and this part of the initial parameter corresponding to the first priority coefficient greater than the first threshold value is taken as an input adjustment bit for obtaining an initial value set based on the actual engineering situation. In order to avoid causing excessive setting workload, the second priority coefficient less than or equal to the first threshold value in the initial parameters is taken as a calculation fill-in bit for obtaining data generated through a calculation rule. Through the above processing, the initial parameters with high priority coefficients are set with initial values meeting the actual requirements, and the initial parameters with low priority coefficients are taken as dominated initial parameters and generated according to the set calculation rule, which is beneficial to improving the efficiency of generating the model while ensuring the degree of fitting to the actual modeling requirements.

[0057] It should be noted that the initial parameter with a large priority coefficient has a one-way dominant effect on the initial parameter with a low related priority coefficient, and the value or attribute of the initial parameter with a low priority coefficient is changed according to the set calculation rule.

[0058] Still need to be explained, when the initial parameter is actively adjusted by the user, the initial parameter to be actively adjusted is taken as the adjustment input bit, and the corresponding weight is temporarily higher than the remaining initial variables with the same priority coefficient, and has the power to dominate the related initial variables of the weight value. And in order to avoid data conflicts, classify units according to the first classification result, the second classification result, and the third classification result, and there is and only one adjustment input bit in the initial parameter with the same priority coefficient in each classification.

[0059] In step S110, the target parameter values corresponding to the plurality of initial parameters are three-dimensionally modeled to obtain a target three-dimensional model.

[0060] It can be understood that in the case where the initial parameters and the target parameter values are determined, the three-dimensional modeling conditions are met, and finally the target three-dimensional model is obtained.

[0061] In an optional embodiment, the three-dimensional modeling of the target parameter values corresponding to the plurality of initial parameters to obtain a target three-dimensional model comprises: determining whether the priority coefficients corresponding to the plurality of initial parameters are greater than a preset second threshold; if the priority coefficients corresponding to the plurality of initial parameters are greater than the second threshold, determining the to-be-checked parameters in the plurality of initial parameters whose priority coefficients are greater than the second threshold; taking the target parameter values corresponding to the to-be-checked parameters as to-be-checked values; determining whether the to-be-checked values meet a preset checking rule; if the to-be-checked values meet the preset checking rule, three-dimensionally modeling the target parameter values corresponding to the plurality of initial parameters to obtain the target three-dimensional model.

[0062] It can be understood that for the generated target three-dimensional model, there is a possibility that the model accuracy is low or even the model is wrong due to large deviation of the target parameter value, and the initial parameters need to be checked. However, it is too cumbersome to check all of them, and the purpose of generating a model to improve efficiency is lost, therefore, the to-be-checked parameters with priority coefficients greater than the second threshold are checked to ensure that their data range is within a reasonable range and their data relationship is in a correct proportion or size relationship. In the case where the to-be-checked parameters meet the checking rule, the initial parameters dominated by the to-be-checked parameters are also considered to meet the checking rule, which reduces the workload of checking, and then the target three-dimensional model is obtained by three-dimensionally modeling the target parameter values corresponding to the plurality of initial parameters.

[0063] According to the above embodiments and specific examples, the application further provides a specific embodiment, for the convenience of illustration, taking the shaft used for the cable tunnel as an example, modeling the shaft, Figure 2 is a flow chart of an optional modeling method of the structure according to an embodiment of the application, and the specific steps are described as follows:

[0064] In step S202, data collection and analysis, the software platforms and model data formats involved in the existing cable engineering three-dimensional design are statistically analyzed to determine the model formats that can be interacted by different three-dimensional design software platforms.

[0065] In step S204, according to the cable engineering three-dimensional design process, the model data interaction process of different three-dimensional design software is determined in combination with the existing three-dimensional design habits of the design unit.

[0066] In step S206, according to the above steps S202 and S204, the barrier of different data platforms is broken through by taking Pmodel (a data format in Python programming language) as an intermediate format to realize the data interaction of three-dimensional models.

[0067] In step S208, according to the existing model types of the cable three-dimensional design software model library and the actual cable engineering commonly used shafts, the modeling range of the cable tunnel shaft is determined.

[0068] In step S210, according to the cable tunnel shaft modeling range determined in step S208, a shaft three-dimensional model parameter database is created, and actual three-dimensional model data is collected to fill the database, which also includes the following multiple specific sub-steps:

[0069] In step S2101, database content preparation, actual shaft information is collected, and hierarchical, classified, named and other parameter screening information of the shaft information are set to facilitate parameter extraction during modeling;

[0070] In step S2102, data collection and category feature point assignment, all shaft information is assigned a first classification according to the type of tunnel shaft: general shaft, special-shaped shaft;

[0071] In step S2103, data collection and category feature point assignment, all shaft information is assigned a second classification according to the shaft construction method: open shaft, underground excavation shaft, shield shaft, pipe jacking shaft;

[0072] In step S2104, data collection and category feature point assignment, the actual name information of a single shaft is assigned a third classification, such as 3M*4M straight shaft, φ4M three-way shaft;

[0073] Step S2105, splitting and naming of data, the shaft shape on the shaft drawing is layered and each part is uniformly named, such as shaft cavity, soil layer, branch segment, connection segment, shaft wall, cushion layer, protection layer, etc.;

[0074] Step S2106, other feature point assignment of data, the data named in step S2105 is assigned feature points according to the first classification result, the second classification result, and the third classification result as the classification unit, Figure 3 is a data schematic diagram of an optional structure modeling method according to an embodiment of the present application, as shown in Figure 3 The above-mentioned feature points can be set to, for example, weight value, explicitness, variability (divided into variable, random, and fixed), attribute type (decimal, integer, text, Boolean, drop-down, etc.), readability, calculation rule (mathematical relationship such as addition, subtraction, multiplication, division, and trigonometric function), initial value, etc.

[0075] Step S2107, generation of the correlation between data, the correlation between variables is described in a tree diagram according to the weight value and the variability, and the basic rules of the tree diagram are as follows: the weight value determines the position of the variable on the tree diagram, the variable with a high weight value is taken as a parent node, and a secondary variable with a low weight value is derived, the variables with the same weight value and mutual influence are at the same node level, and thus the weight relationship and the grouping state between the variables are obtained; the variable with variability in E6 has no parent node and sibling node, the variable with variability as variable has a parent node or a sibling node, and the variable with variability as fixed exists alone and is isolated from the tree diagram.

[0076] Step S212, using the target parameter value corresponding to the initial parameter obtained above, the Python modeling technology of the three-dimensional model is put in to create a shaft model that meets the parameterizable adjustment design, which further includes the following multiple specific sub-steps:

[0077] Step S2121, the shaft model is extracted from the historical structure data set in the form of a class (classification of the built model) to preliminarily encapsulate the data and open a storage space for the variable and shape parameters of the shaft model, and the model general attribute is obtained;

[0078] Step S2122, based on the above-mentioned first classification processing, second classification processing, and third classification processing, and the naming standardization processing of the initial parameter, the attribute segment of the shaft model is set, and the variable attribute is assigned with preset values, read-only, visibility, classification, drop-down menu format, and annotation features according to the database feature points;

[0079] Step S2123, the attribute section of the shaft model is preferentially associated with pre-processing, the variable structure of the optimized shaft model is sorted out and analyzed, the weight value of the database in step S2106 is combined, and the target association information in step S2107 is combined. The initial parameters are assigned priority coefficients. The initial parameters with high priority coefficients, i.e. greater than the first threshold value, can unidirectionally dominate and change the initial parameters with low priority coefficients. Among them, the initial parameters with the highest priority coefficient are only affected by user adjustment and do not require special processing. The initial parameters with medium priority coefficients are affected by the parent node in a mathematical relationship formula as a rule. The initial values should be set in step S2106, and then the data is formatted in the modeling stage according to the mathematical relationship formula in step S2106. Finally, the real-time data is returned. When the initial parameters with the same priority coefficients affect each other, the adjustment input bit affected by the user adjustment is given a higher priority. If the initial parameter is actively adjusted by the user, the bit is the adjustment input bit of the user at this time. The corresponding weight value will be temporarily higher than the initial parameters with the same priority, and has the right to dominate downward. And the same priority coefficient is checked. Each group of initial parameters with the same priority coefficient has and only has one adjustment input bit. The other initial parameters with the same priority coefficient are processed according to the above variable step.

[0080] Step S2124, the attribute constraint rule of the shaft model is read, and the attributes sorted out in step S2123 are read according to the model class built in step S2106 to perform constraint processing on the calculation rules in the database in step S2106.

[0081] Step S2125, the initial parameter checking rule of the shaft model is set, and the attribute section is inserted into the geometric relationship according to the tree priority order in step S2107. The variables at the end of each tree branch start read-only attributes. The initial parameters with high priority coefficients are checked to ensure that the range of the to-be-checked parameters is within a reasonable range, and the data relationship between them is in a correct proportion or size relationship. If not, an error prompt information is sent.

[0082] Step S2126, the shaft model shape is pretreated, the whole is divided into parts, the model is disassembled into basic shape groups, and is sequentially divided into shaft body (primary lining, protection layer, secondary lining), connecting section (primary lining, protection layer, secondary lining), branch section (primary lining, protection layer, secondary lining), shaft bottom (primary lining, protection layer, secondary lining), shaft top, shaft cavity, floor plate, crawling ladder, sump, pedestrian walkway, cable support and the like; the shape is approved and is associated to the attribute section in step S2123, basic body geometry is generated through a called shape modeling function, the attribute section is hung on the geometry in combination with size parameters, and the generation of the basic shape group under multiple modes is sequentially realized; the basic shape group is sequentially arranged and assembled to the corresponding position through a called displacement matrix calculation class function in combination with position parameters, and the required position parameters are associated to the attribute section in step S2123; the model shape is trimmed and the target model is generated; similar model codes are integrated and called as functions, for example, the primary lining, protection layer and secondary lining of the shape group are similar in appearance and only have size differences, and repeated codes should be refined, different size data is taken as a function input, and the corresponding model shape group is returned;

[0083] Step S2129, the model is packaged, and the target three-dimensional model is output to three-dimensional model processing software for visual processing.

[0084] Step S214, after the model is drawn, the three-dimensional model geometry data and attribute data are integrated, the target three-dimensional model is exported into a data format required by interaction demand, and the interactive use of model data is realized.

[0085] Figure 4 It is another optional construction modeling method according to an embodiment of the present application, as shown in the flow chart of Figure 4 In order to facilitate data interaction between different platforms, the following steps are specifically provided:

[0086] Step S402, data collection and analysis, the software platforms and model data formats involved in existing cable engineering three-dimensional design are statistically analyzed to determine the model formats that can be interacted with different three-dimensional design software platforms; the data interface and data interaction of each platform are investigated, the formats that can be imported and exported supported by each platform and the formats that can be interacted with data are determined, and it is verified whether the model information carried is complete and can be received and used by the next platform.

[0087] Step S404, according to the cable engineering three-dimensional design process, in combination with the existing three-dimensional design habit of the design unit, the process of model data interaction of different three-dimensional design software is determined; the existing design process and habit of the cable engineering are investigated, the breakthrough point of model data interaction is found for the existing design process, and how to completely import the cable shaft model data generated by different three-dimensional design platforms into the existing three-dimensional graphic platform is explored.

[0088] Step S406, according to the above steps, determine to break through the wall of different data platform with Pmodel as the intermediate format, realize the data interaction of three-dimensional model. Figure 5 is an interactive flowchart of an optional construction modeling method according to an embodiment of the application, as shown in Figure 5 The initial parameter data is circulated by the data interface of the three-dimensional graphics platform 1, and the data is transmitted to the three-dimensional graphics platform 3 by the intermediate format Pmodel of the three-dimensional graphics platform 2, and the data interface of the three-dimensional graphics platform 3 is used as a bridge for model interaction, and the shaft model generated by the platform is exported to the existing design platform process.

[0089] Step S408, according to the existing model library of the cable three-dimensional design software model and the actual cable engineering commonly used shaft, determine the cable tunnel shaft modeling range; according to the general design file of the cable tunnel and the actual engineering commonly used cable shaft, two categories are summarized and arranged, namely general shaft and special-shaped shaft.

[0090] Step S410, according to the cable tunnel shaft modeling range determined in step S408, create a shaft three-dimensional model parameter database, and collect actual three-dimensional model data to fill the database; according to the commonly used cable tunnel shaft, the characteristics of shaft model parameters are summarized and refined according to different types, structural characteristics, application conditions and design methods; the refined feature points and rules are stored in the database for easy extraction and calling during shaft modeling.

[0091] Step S412, use the data in the three-dimensional model parameter database of step S410 to place into the Python modeling technology of three-dimensional model, create a shaft model that meets the parameterized adjustment design; extract the information of the database in step S410 into code language and model attributes, convert the deformation rule into mathematical formula, use Python modeling technology to create model shape, and associate the information of the database, deformation rule and model geometry as necessary; in order to be closer to the actual engineering project and improve the rationality and usability of the shaft model, the built-in data legality, deformation restriction and other checks and rules of Python modeling technology are also used, and finally the geometry and attributes are packaged together to transmit the data to the three-dimensional graphics platform 3.

[0092] Step S414, after the completion of the model, the geometric data and attribute data of the three-dimensional model are integrated, and the three-dimensional model is exported to the data format of the three-dimensional graphics platform 2, so that the model data can be interactively used. After receiving the data sent by the Python modeling technology, the three-dimensional graphics platform 2 saves the data and makes the model shaping and attribute explicit according to the data, uses the arrangement tool of the three-dimensional graphics platform to cooperate with the functions such as capturing and moving, places the shaft model in a suitable position, adjusts the value of the explicit attribute to correct the shaft shaping, and until the shaft model meets the design requirements, the data interface function of the three-dimensional graphics platform is used to export the data to the three-dimensional graphics platform 2 for display.

[0093] The embodiment of the application can achieve at least one of the following beneficial effects: a tunnel shaft three-dimensional model with high degree of freedom and high flexibility can be provided, and the attribute constraints of the three-dimensional model are not limited to changes in component size, but also include component visibility and modeling type; the overall idea is to summarize the influence of various common working conditions on the three-dimensional model into rules, and summarize these rules into one or more attribute variables that cooperate or restrict each other, the attribute variables are embedded in the attribute table, and the rules are written in the Python code. After the software creates a geometric model, the attribute table of the three-dimensional model is called to display the attribute variables set in the code, so that only the attribute variables need to be modified, and the software will drive the shape change of the three-dimensional model according to the set rules. In the code, only simple variable definition, function call and mathematical formula are needed to build a rule, so the shaft model has the advantages of high flexibility, strong adaptability, simple and fast creation, etc. The three-dimensional software underlying data engine has various data interfaces, allowing direct import or indirect import through an intermediate format to interact with different three-dimensional design software model data, or export the model designed by the three-dimensional software to other three-dimensional design software platforms, thereby realizing the interactive use and model sharing of different model data.

[0094] According to another aspect of the embodiment of the application, a structure modeling device is also provided, Figure 6 is a schematic diagram of an optional structure modeling device according to an embodiment of the application, as Figure 6 shown, the device comprises a first determination module 602, a classification module 604, a second determination module 606, a third determination module 608, and an acquisition module 610. The device will be described below.

[0095] The first determination module 602 is configured to determine a plurality of initial parameters and historical data corresponding to the plurality of initial parameters based on a historical structure data set, wherein the historical structure data set is obtained based on data of a structure of a preset type in a historical construction project.

[0096] The classification module 604 is connected with the first determination module 602, and is configured to perform classification processing on the plurality of initial parameters to obtain target correlation information between the plurality of initial parameters.

[0097] The second determination module 606 is connected with the classification module 604, and is configured to determine priority coefficients corresponding to the plurality of initial parameters according to the target correlation information.

[0098] The third determination module 608 is connected with the second determination module 606, and is configured to determine target parameter values corresponding to the plurality of initial parameters based on the historical data and the priority coefficients.

[0099] The acquisition module 610 is connected with the third determination module 608, and is configured to perform three-dimensional modeling on the target parameter values corresponding to the plurality of initial parameters to obtain a target three-dimensional model.

[0100] In the embodiment of the present application, the first determination module 602 is configured to determine a plurality of initial parameters and historical data corresponding to the plurality of initial parameters based on a historical construction data set, wherein the historical construction data set is data of a construction of a preset type in a historical construction project; the classification module 604 is connected with the first determination module 602, and is configured to perform classification processing on the plurality of initial parameters to obtain target correlation information between the plurality of initial parameters; the second determination module 606 is connected with the classification module 604, and is configured to determine priority coefficients corresponding to the plurality of initial parameters according to the target correlation information; the third determination module 608 is connected with the second determination module 606, and is configured to determine target parameter values corresponding to the plurality of initial parameters based on the historical data and the priority coefficients; and the acquisition module 610 is connected with the third determination module 608, and is configured to perform three-dimensional modeling on the target parameter values corresponding to the plurality of initial parameters to obtain a target three-dimensional model. The purpose of automatically generating a construction model based on historical data is achieved, thereby realizing the technical effects of improving modeling efficiency, improving design interactivity, and reducing consumption of human resources, and thereby solving the technical problem of large differences between generated three-dimensional models due to the great influence of a construction on a specific environment, low modeling efficiency, and high consumption of human resources.

[0101] It should be noted that the first determination module 602, the classification module 604, the second determination module 606, the third determination module 608, and the acquisition module 610 correspond to steps S102 to S110 in the embodiment, and have the same instances and application scenarios as the corresponding steps, but are not limited to the contents disclosed in the above embodiment. It should be noted that the modules can run in a computer terminal as part of the device.

[0102] It should be noted that the optional or preferred embodiments of the present embodiment can refer to the related description in the embodiments, which will not be repeated here.

[0103] The above structure modeling device can further include a processor and a memory, the first determining module 602, the classification module 604, the second determining module 606, the third determining module 608, the acquisition module 610 and the like are stored in the memory as program units, and the above program units stored in the memory are executed by the processor to realize the corresponding functions.

[0104] The processor includes a core, and the core calls the corresponding program unit in the memory. The core can be one or more. The memory can include a non-persistent memory in a computer readable medium, a random access memory (RAM) and / or a non-volatile memory such as a read-only memory (ROM) or a flash memory (flash RAM), and the memory includes at least one memory chip.

[0105] The embodiment of the present application provides a storage medium, which stores a program, and the program is executed by a processor to realize the structure modeling method.

[0106] The embodiment of the present application provides an electronic device, which includes a processor, a memory and a program stored in the memory and executable on the processor, and the processor executes the program to realize the following steps: determining a plurality of initial parameters and historical data corresponding to the plurality of initial parameters based on a historical structure data set, wherein the historical structure data set is obtained based on data of a structure of a preset type in a historical construction project; performing classification processing on the plurality of initial parameters to obtain target association information between the plurality of initial parameters; determining priority coefficients corresponding to the plurality of initial parameters respectively according to the target association information; determining target parameter values corresponding to the plurality of initial parameters respectively based on the historical data and the priority coefficients; and performing three-dimensional modeling on the target parameter values corresponding to the plurality of initial parameters respectively to obtain a target three-dimensional model. The device in the present application can be a server, a PC or the like.

[0107] The application further provides a computer program product, which is suitable for executing a program of the following method steps when executed on a data processing device: determining a plurality of initial parameters and historical data corresponding to the plurality of initial parameters based on a historical structure data set, wherein the historical structure data set is obtained based on data of a historical construction project of a preset type of structure; performing classification processing on the plurality of initial parameters to obtain target correlation information between the plurality of initial parameters; determining priority coefficients corresponding to the plurality of initial parameters respectively according to the target correlation information; determining target parameter values corresponding to the plurality of initial parameters respectively based on the historical data and the priority coefficients; and performing three-dimensional modeling on the target parameter values corresponding to the plurality of initial parameters respectively to obtain a target three-dimensional model.

[0108] The above-mentioned embodiment numbers of the application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0109] In the above-mentioned embodiments of the application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0110] In several embodiments provided in the present application, it should be understood that the disclosed technical contents can be implemented by other ways. Among them, the above-mentioned device embodiments are only schematic, for example, the division of the above-mentioned units can be a logical function division, and actual implementation can have another division way, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or modules shown or discussed can be indirect coupling or communication connection through some interfaces, units or modules, which can be electrical or other forms.

[0111] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed to a plurality of units. Part or all of the units can be selected to achieve the purpose of the embodiment scheme according to actual needs.

[0112] In addition, each functional unit in each embodiment of the application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be realized in the form of hardware or in the form of software functional unit.

[0113] The integrated unit described above, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in other words, the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the above-mentioned method of each embodiment of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0114] The above is only the preferred embodiment of the present application, and it should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A method for modeling structures, characterized in that, include: Based on the historical structure dataset, multiple initial parameters and corresponding historical data are determined. The historical structure dataset is obtained from data on historical construction projects based on a preset type of structure. The process involves classifying the multiple initial parameters to obtain target association information among them, including: performing primary classification on the multiple initial parameters based on construction type to obtain primary classification results for each initial parameter; performing secondary classification on the multiple initial parameters based on construction method to obtain secondary classification results for each initial parameter; performing tertiary classification on the multiple initial parameters based on historical naming method to obtain tertiary classification results for each initial parameter; determining feature information corresponding to each initial parameter based on the primary classification results, the secondary classification results, and the tertiary classification results, wherein the feature information includes at least weight values; and obtaining the target association information among the multiple initial parameters based on the weight values. Based on the target association information, determine the priority coefficients corresponding to the plurality of initial parameters respectively; Based on the historical data and the priority coefficients, determining the target parameter values ​​corresponding to the plurality of initial parameters includes: determining a first priority coefficient among the priority coefficients corresponding to the plurality of initial parameters that is greater than a preset first threshold; using the initial parameter corresponding to the first priority coefficient as an adjustment input bit, wherein the adjustment input bit is used to obtain a preset initial value; determining a second priority coefficient among the priority coefficients corresponding to the plurality of initial parameters that is less than or equal to the first threshold; using the initial parameter corresponding to the second priority coefficient as a calculation input bit, wherein the calculation input bit is used to obtain data generated by calculation based on a preset calculation rule; determining the input value corresponding to the calculation input bit based on the historical data, the calculation rule, and the target association information; and obtaining the target parameter values ​​corresponding to the plurality of initial parameters according to the initial value and the input value. The target parameter values ​​corresponding to the multiple initial parameters are used for three-dimensional modeling to obtain the target three-dimensional model.

2. The structure modeling method according to claim 1, characterized in that, The step of obtaining the target association information among the plurality of initial parameters based on the weight values ​​includes: The initial parameter whose weight value is greater than a preset weight threshold among the plurality of initial parameters is determined as the parent node, wherein the parent node is the superior parameter in the target association information; The initial parameters whose weight values ​​are less than or equal to the weight threshold among the plurality of initial parameters are determined as child nodes, wherein the child nodes are lower-level parameters in the target association information; Based on the parent node and the child node, the target association information among the plurality of initial parameters is determined.

3. The structure modeling method according to claim 2, characterized in that, When the feature information includes variability information, and the variability information includes variable variables and arbitrary quantities, wherein the variable variables are variables among the plurality of initial parameters and are not dependent variables, and the arbitrary quantities are variables among the plurality of initial parameters and are dependent variables, determining the target association information between the plurality of initial parameters based on the parent node and the child node includes: Based on preset constraints, the parent node and the child node are filtered to obtain the parent node and the child node that satisfy the constraints. The constraints are: the initial parameter corresponding to the variable does not exist in the parent node and the sibling node, and the initial parameter corresponding to the arbitrary variable exists in at least one of the parent node or the sibling node. The sibling node is the initial parameter with the same weight value among the multiple initial parameters. Based on the parent node and the child node that satisfy the constraints, the target association information among the plurality of initial parameters is determined.

4. The structure modeling method according to claim 1, characterized in that, The step of performing three-dimensional modeling based on the target parameter values ​​corresponding to the plurality of initial parameters to obtain a target three-dimensional model includes: Determine whether the priority coefficients corresponding to the plurality of initial parameters are greater than a preset second threshold; If the priority coefficient corresponding to the plurality of initial parameters is greater than the second threshold, then the parameter to be checked among the plurality of initial parameters whose priority coefficient is greater than the second threshold is determined; The target parameter value corresponding to the parameter to be verified is taken as the value to be verified. Determine whether the value to be checked meets the preset check rules; If the value to be verified meets the preset verification rules, then three-dimensional modeling is performed based on the target parameter values ​​corresponding to the multiple initial parameters to obtain the target three-dimensional model.

5. The method for modeling structures according to any one of claims 1 to 4, characterized in that, The determination of multiple initial parameters and corresponding historical data based on the historical structure dataset includes: Obtain the various data formats corresponding to the historical structure dataset; Determine an intermediate format corresponding to the multiple data formats, wherein the intermediate format is a data format that has the ability to interact with all of the multiple data formats; Based on the historical structure dataset and the intermediate format, the plurality of initial parameters and the historical data corresponding to the plurality of initial parameters are determined.

6. A structure modeling device, characterized in that, include: The first determining module is used to determine multiple initial parameters and historical data corresponding to the multiple initial parameters based on a historical structure dataset, wherein the historical structure dataset is obtained based on data of a preset type of structure in historical construction projects; A classification module is used to classify the plurality of initial parameters to obtain target association information among the plurality of initial parameters, including: performing primary classification processing on the plurality of initial parameters based on construction type to obtain primary classification results corresponding to the plurality of initial parameters respectively; performing secondary classification processing on the plurality of initial parameters based on construction method to obtain secondary classification results corresponding to the plurality of initial parameters respectively; performing tertiary classification processing on the plurality of initial parameters based on historical naming method to obtain tertiary classification results corresponding to the plurality of initial parameters respectively; determining feature information corresponding to the plurality of initial parameters respectively based on the primary classification results, the secondary classification results, and the tertiary classification results, wherein the feature information includes at least weight values; and obtaining the target association information among the plurality of initial parameters based on the weight values. The second determining module is used to determine the priority coefficients corresponding to the plurality of initial parameters based on the target association information. The third determining module is used to determine the target parameter values ​​corresponding to the plurality of initial parameters based on the historical data and the priority coefficients, including: determining a first priority coefficient among the priority coefficients corresponding to the plurality of initial parameters that is greater than a preset first threshold; using the initial parameter corresponding to the first priority coefficient as an adjustment input bit, wherein the adjustment input bit is used to obtain a preset initial value; determining a second priority coefficient among the priority coefficients corresponding to the plurality of initial parameters that is less than or equal to the first threshold; using the initial parameter corresponding to the second priority coefficient as a calculation input bit, wherein the calculation input bit is used to obtain data generated by calculation based on a preset calculation rule; determining the input value corresponding to the calculation input bit based on the historical data, the calculation rule, and the target association information; and obtaining the target parameter values ​​corresponding to the plurality of initial parameters based on the initial value and the input value. The acquisition module is used to perform three-dimensional modeling on the target parameter values ​​corresponding to the multiple initial parameters respectively, so as to obtain the target three-dimensional model.

7. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the structure modeling method according to any one of claims 1 to 5.

8. A processor, characterized in that, The processor is used to run a program, wherein the program executes the structure modeling method according to any one of claims 1 to 5 when it runs.

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